A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

• Published in: Advances in operations research 2018, Vol.2018 (2018), p.1-20
• Main Authors: Nikfarjam, Hava, Noura, Abbasali, Rostamy-Malkhalifeh, M.
• Format: Article
• Language: English
• Subjects: Component and supplier management; Data analysis; Data envelopment analysis; Decision analysis; Decision making; Efficiency; Fuzzy sets; Managers; Methods; Operations research; Optimization; Social responsibility; Suppliers; Supply chain management; Sustainability
• ISSN: 1687-9147; 1687-9155
• DOI: 10.1155/2018/7625025

Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans.